Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.

Addresses two shortcomings of service quality empirical research. Investigates the importance of service quality as a predictor of actual choice behaviour and examines the importance of process and outcome quality attributes as predictors of choice. Uses regression analysis to investigate the importance of service quality attributes on choice. Suggests that consumers utilise multiple process and outcome quality attributes in their choices.

The question of how an auditor's going-concern disclosure affects a client's future operations has long troubled the auditing profession. In an attempt to provide further understanding of this issue, we introduce Discrete-Time Survival Analysis (DTSA) to examine the aftermath of 23 1 first-time going-concern disclosures on clients' subsequent continuance. DTSA represents a significant refinement over traditional ordinary least squares (OLS) and logistic (LOGIT) regression in that it provides not only a probability estimate, but also an estimate of the timing of the event occurrence. The addition of this extra dimension (event timing) aids decision makers by providing more complete information about event probabilities.Consistent with the "self-fulfilling prophecy effect," the risk profiles developed from DTSA indicate that the first year subsequent to the initial going-concern disclosure was the most dangerous in terms of risk of bankruptcy. However, after the first year, the incidence of bankruptcy decreases significantly. Thus, DTSA is able to provide a richer perspective on this perplexing issue than previously considered.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

No abstract